Sathish N, FSS – Tech Observer
With open banking and instant payments increasingly becoming mainstream, back-office enterprise reconciliation systems need to keep pace. Conventionally, transactions typically were processed in a batch mode and payments took hours, if not days, to process, clear and settle. Now, reconciliation and settlement cycles have been compressed. This puts tremendous pressure on any institution’s back office to support multiple intraday settlement cycles and reconcile data in near real-time.
That is why financial institutions are looking for end-to-end enterprise level automated reconciliation processes that can help them scale to handle large influx of transaction data, improve speed, manage operational risk, and address compliance needs.
According to Sathish N, Deputy Chief Product Officer, FSS this is what AI and Machine Learning promise to deliver. “By employing machine learning at key data reconciliation points, reconcilers can unlock multiples of value in terms of time, operating cost and avoiding regulatory penalties,” he said in an interview with Tech Observer, adding that advanced ML algorithms can improve process efficiency across multiple reconciliation points.
How automating reconciliation systems helps in improving the efficiency of processing transactions?
With digital payments growing exponentially, millions of transactions are exchanged daily between multiple payment ecosystem constituents. The payment or transaction settlement cycles varies basis the combination of stakeholder and different applications that are used and accounting records maintained by these multiple processing systems need to be in sync at different stages of the transaction. The accuracy of the financial close process is crucial to maintaining the financial integrity of the ecosystem, mitigating risk, and fostering trust amongst customers.
Further with open banking and instant payments increasingly becoming mainstream, back-office enterprise reconciliation systems need to keep pace. Conventionally, transactions typically were processed in a batch mode and payments took hours, if not days, to process, clear and settle. Now, reconciliation and settlement cycles have been compressed. This puts tremendous pressure on any institution’s back office to support multiple intraday settlement cycles and reconcile data in near real-time. Current manual or semi-automated processes simply cannot scale to accommodate new business needs.
End-to-end enterprise level automated reconciliation processes can help financial institutions and corporates scale to handle large influx of transaction data, improve speed, manage operational risk, and address compliance needs.
Improve Accuracy and Lower Risk of Error
A single exception can result in significant losses and reconciliation teams handle a large number of exceptions every day Automating reconciliation and certification processes throughout the entire financial close lifecycle, reduces the risk of errors.
Lower Exceptions and Write-Offs
With automated reconciliation processes accounting discrepancies can be proactively identified and corrected before customers even register a complaint. As an example, the customers could have cancelled a transaction, but the corresponding credit may have not been received due to a technical glitch or a system error or an actual fraud that has occurred. With detailed audit trails such discrepancies can be easily identified, enabling banks to reduce exception investigation handling time by 90%, optimize dispute handling costs which in turn aids with risk mitigation
Mitigate Compliance Risk
With improved data management and audit trails, financial institutions reduce compliance risk and ensure compliance with audit and regulatory requirements.
Automate time-consuming manual processes in reconciliation operations, saves time staff spends on reconciliation processes, freeing resources to focus on strategic added value work including risk mitigation, and operational improvements
How AI and ML could be used by banks to overcome the challenges in reconciliation systems?
A growing number of channels, instrument complexity, and activity spread across multiple service providers and increased transaction frequency by consumers adds to the complexity of the reconciliation process. AI and Machine Learning will have a significant upside on the efficiency of the reconciliation process. By employing machine learning at key data reconciliation points, reconcilers can unlock multiples of value in terms of time, operating cost and avoiding regulatory penalties,
Advanced ML algorithms can improve process efficiency across multiple reconciliation points. The reconciliation process typically entails tasks such as onboarding payment classes, extracting, and normalizing data from non-standardized file formats, defining matching rules and posting entries for settling accounts.
Conventional systems rely on a static pre-configured “rule-based framework” for payments reconciliation. However, these tools can become inefficient while adding new data sources or if new entries are introduced in a particular reconciliation file, these need to be identified manually. Further reconciliation teams need to create, test, and implement new rules whilst balancing the impact on existing rules which prolongs the reconciliation cycle time. With ML-enabled processes, the system automatically “learns” the data sources and patterns, analyzes it for likely matches across multiple data sets, highlights reconciliation exceptions / mismatches, and presents actionable “to do” lists to resolve data issues.
The use of Robotic Process Automation can automate routine, manually intensive tasks. Let me give you an example. Even today banks with automated reconciliation processes deploy dedicated personnel to fetch files from an interchange portal or a dispute management system, download the files and place them in the right location for the reconciliation system to act on the data. Such tasks can be automated by use of bots, maximizing value of employee time.
Payment reconciliations have become exceedingly complex, with multiple payment options, channels, combination of product processors for different payment method across line of business and the need for speed and accuracy of reconciliation is crucial for businesses. FSS Smart Recon offers an AI-based solution for reconciliation management across payment workflows, with built in support for, multi-source, multi-file many-to-many reconciliation scenarios. With FSS Smart Recon customers can achieve a 40% improvement in time to market for greenfield implementations, a sizable 30% betterment in reconciliation time cycles, and an overall 25% lessening in direct costs as compared to partially automated processes FSS Smart Recon adds value in the following ways:
- A unified platform for providing a modern, fully web-based reconciliation platform system to handle end-to-end reconciliation which incorporates data import, transformation and enrichment, data matching, exception management
- Wide application – Supports all classes of digital payments using a single system – General Ledger Reconciliation Tally, ATM Reconciliation, Card Reconciliation, Online Payments, Wallets, Instant Payments (IMPS and UPI), NEFT, RTGS and QR Code Payments — with built-in flexibility to rapidly onboard new payment channels and schemes
- Universal Data Wizard: Simplifies set-up of the reconciliation process via a template-based data-mapping framework. This optimizes go live time for greenfield implementations by 30 per cent
- Detailed Audit Trail: Provides a detailed audit trail helps users understand the rationale behind a break or match case and address it accordingly.
- Advanced Exception Identification and analysis for advising timely action and follow ups to enable closure of the same
- AI-based Settlement Processes Leveraging Machine Learning (ML), algorithms, FSS Smart Recon continually learns file patterns and can automatically identify new records, enabling staff to predict exceptions and perform resolution actions, without the need of constant support or professional services.
- Dispute Management – Support for dispute and chargeback lifecycle enabling banks to respond to disputes in much shorter time frames – enhancing efficiency as well as customer service.
- Flexible Business Models: FSS offers Recon services as a licensed and a SaaS model, d to provide greater deployment flexibility to customers, eliminating the need for upfront capital expenditure and
What are key technology trends are you observing in reconciliation space?
Rapid payments evolution, market competition, and advancements in technology continue drive evolution and modernization of reconciliation processes. Technology trends that are gaining momentum include
- Greater adoption of SaaS and cloud-based models to accommodate growing transaction workloads and to lower total cost of ownership
- Blockchain is a perfect choice for complex reconciliation and would be the next differentiating inclusion in global leading products
- Enhanced use of AI and Machine Learning AI-based algorithms for self- supervised and self-optimizing recon processes
- Smart use of data by designing the right data layer or system of record layer to to improve performance, precision of matching , operations, and fraud controls
What would be the upcoming focus areas for FSS?
Our next big launch is around analytics and data science, the wealth of data today in most large organizations is pushed to a Data Lake or a warehouse and very little is being done to leverage these insights to make an impact to your customers or business. The product is designed to address this specific Big Data opportunity in the payments space. The product is a complete persona-based analytics suite that comes with predefined insights by business product areas, the matrix keeps growing and will soon map the entire payment ecosystem. The product helps banks to make data-driven business decisions, enhance productivity and business efficiency.